Enhanced Classification of Sugarcane Diseases Through a Modified Learning Rate Policy in Deep Learning
Bibliographic record
Abstract
Motivated by the agriculture-centric economy of India, and specifically the challenges experienced in the sugarcane sector due to reduced yields from diseases including rust, red rot, yellow leaf, and mosaic, this study aims to harness effective deep-learning technologies for improved plant disease monitoring.The challenge of mitigating over-fitting, particularly when dealing with small datasets, is addressed through hyper-parameter tuning.In this study, we introduce an innovative modification to the learning rate decay policy, tested on a uniquely constructed small-sized database of sugarcane leaf images.This database encompasses five classes: healthy, rust, red rot, yellow leaf, and mosaic.To evaluate the effectiveness of the proposed learning rate policy, it was compared against multiple benchmark datasets and found to surpass established methods in performance metrics.This study introduces an additional exponential component into the learning rate policy to facilitate model convergence within the same number of epochs, thereby enhancing its performance over step, exponential, cosine, and exponential sine methods.A marginal improvement in scores was observed with the integration of the proposed learning rate policy and MobileNet-V2 as the backbone architecture.Remarkably, the MNIST dataset achieved a score of 99.9%, CIFAR-10 scored 92%, and the newly introduced database secured a score of 89%.These results underscore the efficacy of the proposed approach in enhancing the classification of sugarcane diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".